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A scale adaptive generative target tracking method based on modified particle filter

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Abstract

This paper proposes an advanced particle filter (PF) algorithm based on the quantum particle swarm optimization method (QPSO) and adaptive genetic algorithm (QAPF). After resampling of the PF, the position updating equation of the QPSO is applied to improve the particle distribution. Then replace the individuals with lower fitness with those with higher fitness. The genetic operation from the adaptive genetic algorithm (AGA) is then applied to increase the accuracy and sample diversity. An frame size adaptive adjustment model is proposed to reduce the number of useless features and improve the accuracy of target positioning. Multiple simulations of the nonlinear target tracking model are carried out, and the results demonstrate that the numerical stability, efficiency and accuracy of our QAPF algorithm are significantly better than those of other similar algorithms. QAPF is also compared with similar tracking algorithms via a set of tracking experiments. Our experiments on the OTB-100 dataset prove that the QAPF algorithm is much better than the PF, PF improved by particle swarm optimization (PSO-PF) and PF advanced by genetic algorithm (GAPF) tracking algorithms and other typical generative trackers in terms of the tracking precision, success rate, efficiency and robustness.

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Authors and Affiliations

  1. School of Mechanical and Vehicle Engineering, West Anhui University, Lu’an City, 237012, Anhui, China

    Xiao Yuqi

  2. School of Traffic and Transportation, Chongqing Jiaotong University, Nan’an District, Chongqing, 400074, China

    Wu Yongjun

  3. Zhuzhou Branch, Agricultural Bank of China, Zhuzhou, 412000, Hunan, China

    Yang Fan

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  1. Xiao Yuqi
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  2. Wu Yongjun
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  3. Yang Fan
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Correspondence to Xiao Yuqi.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled “A scale adaptive generative target tracking method based on modified particle filter”. All data generated or analysed during this study are included in this published article.

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Yuqi, X., Yongjun, W. & Fan, Y. A scale adaptive generative target tracking method based on modified particle filter. Multimed Tools Appl 82, 31329–31349 (2023). https://doi.org/10.1007/s11042-023-14901-4

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  • DOI: https://doi.org/10.1007/s11042-023-14901-4

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